Shaofei Jiang

PhD Candidate in Economics at UT Austin

I am a PhD candidate in Economics at the University of Texas at Austin. My research interests are in information economics and game theory.



Abstract: I study a model of costly Bayesian persuasion by a privately and partially informed sender who conducts a public experiment. I microfound the cost of an experiment via Wald's sequential sampling problem and show that it equals the expected reduction in a weighted log-likelihood ratio function evaluated at the sender's belief. I focus on equilibria satisfying the D1 criterion. The equilibrium outcome depends on the relative costs of drawing good and bad news in the experiment. If bad news is more costly, there exists a unique separating equilibrium outcome, and the receiver unambiguously benefits from the sender's private information. If good news is sufficiently more costly, the single-crossing property fails. There may exist a continuum of pooling equilibria, and the receiver strictly suffers from sender private information in some equilibria.

Abstract: Evidence games study situations where a sender persuades a receiver by selectively disclosing hard evidence about an unknown state of the world. Evidence games often have multiple equilibria. Hart et al. (2017) propose to focus on truth-leaning equilibria, i.e., perfect Bayesian equilibria where the sender discloses truthfully when indifferent, and the receiver takes off-path disclosure at face value. They show that a truth-leaning equilibrium is an equilibrium of a perturbed game where the sender has an infinitesimal reward for truth-telling. We show that, when the receiver's action space is finite, truth-leaning equilibrium may fail to exist, and it is not equivalent to equilibrium of the perturbed game. To restore existence, we introduce a disturbed game with a small uncertainty about the receiver's payoff. A purifiable equilibrium is a truth-leaning equilibrium in an infinitesimally disturbed game. It exists and features a simple characterization. A truth-leaning equilibrium that is also purifiable is an equilibrium of the perturbed game.

Abstract: We study a disclosure game with a large evidence space. There is an unknown binary state. A sender observes a sequence of binary signals about the state and discloses a left truncation of the sequence to a receiver in order to convince him that the state is good. We focus on truth-leaning equilibria (cf. Hart et al. (2017)), where the sender discloses truthfully when doing so is optimal, and the receiver takes off-path disclosure at face value. In equilibrium, seemingly sub-optimal truncations are disclosed, and the disclosure contains the longest truncation that yields the maximal difference between the number of good and bad signals. We also study a general framework of disclosure games which is compatible with large evidence spaces, a wide range of disclosure technologies, and finitely many states. We characterize the unique equilibrium value function of the sender and propose a method to construct equilibria for a broad class of games.

Abstract: I study a model of firm dynamics where a firm can invest in product quality and exert efforts to obtain good publicities. The market learns from good publicities without directly observing quality, investment, or efforts. I analyze the relationship between the firm's advertisement and investment incentives. I show that any equilibrium has a threshold structure: the firm invests and advertises when the market belief is low, only advertises for a range of intermediate beliefs, and does neither when the belief is high. I further study whether increased frequency of publicity opportunities will lead to increased investment, and how these results differ when the market learns from bad news.


I was awarded the Outstanding Teaching Assistant Award by the Economics Department in 2020.

As instructor:

As teaching assistant:

  • Introductory Game Theory, Prof. V. Bhaskar, UT Austin (2022)

  • Economics of Money, Prof. Michael Brandl, UT Austin (2021)

  • Microeconomics II (PhD), Prof. Caroline Thomas, UT Austin (2018-20) | Select TA session notes

  • Math for Economists (PhD), Prof. Maxwell Stinchcombe, UT Austin (2019)

  • Probability & Statistics (PhD), Profs. Maxwell Stinchcombe & Haiqing Xu, UT Austin (2017-18)

  • Macroeconomic Theory, Prof. Felipe Schwartzman, UT Austin (2017)

  • Introduction to Microeconomics, Prof. Thomas Wiseman, UT Austin (2016)

  • Applied Econometrics, Prof. Wanchuan Lin, Peking University (2016)